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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations.

Analytics 104
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight.

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23 Inspiring Women to Watch in 2023

TechSee

She is also an experienced National Account Manager with a demonstrated history of working in the hospitality industry. Caroline Yap, Director of AI Practice, Google Cloud – Caroline’s team accelerates customer transformations with AI and Industry Solutions, including Contact Center AI (CCAI), Vertex AI, and DocAI.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

Prerequisites In order to provision ML environments with the AWS CDK, complete the following prerequisites: Have access to an AWS account and permissions within the Region to deploy the necessary resources for different personas. Make sure you have the credentials and permissions to deploy the AWS CDK stack into your account.

Scripts 73
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EVERYTHING YOU NEED TO KNOW ABOUT STIR/SHAKEN

Hodusoft

In later years, STIR/SHAKEN was developed jointly by the SIP Forum and the Alliance for Telecommunications Industry Solutions (ATIS) to efficiently implement the Internet Engineering Task Force (IETF). Malicious actors can still use other methods, such as social engineering, to deceive users and commit fraud.

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

Together, these additions help agronomists, software developers, ML engineers, data scientists, and remote sensing teams provide scalable, valuable decision-making support systems to farmers. She is passionate about bringing cutting-edge use cases to the forefront and helping customers build strategic solutions on AWS.

APIs 74
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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools.